Image processing apparatus that converts image data from line sequence to block sequence, image processing method, and storage medium

ABSTRACT

An apparatus that performs image processing on input image data in parallel in units of block according to an aspect of the invention includes a derivation unit configured to derive a memory size used in conversion processing of converting data alignment sequence in the image data from a line sequence into a block sequence which is executed before rendering of the image data and a memory size used in the conversion processing which is executed after the rendering of the image data, a determination unit configured to determine a timing for executing the conversion processing based on the derivation results, and a conversion unit configured to execute the conversion processing at the determined timing.

BACKGROUND OF THE INVENTION Field of the Invention

The aspect of the embodiments relates to an image processing apparatus,an image processing method, and a storage medium. The aspect of theembodiments particularly relates to a technology for converting imagedata from a line sequence to a block sequence.

Description of the Related Art

A technology has been proposed for converting image data into image datafor each block and performing subsequent image processing in parallel inunits of block to carry out printing at a high speed at the time ofimage forming processing. The processing of converting the image datainto the image data for each block is referred to as block sequenceconversion processing. The block sequence conversion processing isprocessing of dividing image data that has been input to a work bufferin a page line sequence and buffered for a predetermined block heightinto image data for each set of plural blocks to be output in a blocksequence. In this manner, in the block sequence conversion processing,the image data is to be temporarily buffered in the work buffer, and apredetermined buffer size is to be secured.

Japanese Patent Laid-Open No. 2009-269355 describes a technology forstoring image data in a work buffer in a vector system before renderinginstead of a raster system after rendering.

However, in a case where the vector system block sequence conversionprocessing described in Japanese Patent Laid-Open No. 2009-269355 isapplied to page image data where a large number of drawing objectsexist, a memory size of the work buffer may be significantly increasedin some cases.

SUMMARY OF THE INVENTION

An apparatus that performs image processing on input image data inparallel in units of block according to an aspect of the inventionincludes a derivation unit configured to derive a memory size used inconversion processing of converting data alignment sequence in the imagedata from a line sequence into a block sequence which is executed beforerendering of the image data and a memory size used in the conversionprocessing which is executed after the rendering of the image data, adetermination unit configured to determine a timing for executing theconversion processing based on the derivation results, and a conversionunit configured to execute the conversion processing at the determinedtiming.

Further features of the aspect of the embodiments will become apparentfrom the following description of exemplary embodiments with referenceto the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are block diagrams illustrating examples ofconfigurations of a system and an image processing apparatus accordingto a first exemplary embodiment of the present disclosure.

FIG. 2 is a block diagram illustrating an example of a softwareconfiguration of the image processing apparatus according to the firstexemplary embodiment of the present disclosure.

FIG. 3 is a flow chart illustrating processing of the image processingapparatus according to the first exemplary embodiment of the presentdisclosure.

FIG. 4 is a flow chart illustrating intermediate data generationprocessing according to the first exemplary embodiment of the presentdisclosure.

FIG. 5 is a flow chart illustrating rendering processing according tothe first exemplary embodiment of the present disclosure.

FIGS. 6A to 6D are explanatory diagrams for describing closed regiondata generated by the rendering processing according to the firstexemplary embodiment of the present disclosure.

FIG. 7 is an explanatory diagram illustrating an example of a blocksequence conversion method table according to the first exemplaryembodiment of the present disclosure.

FIG. 8 is an explanatory diagram illustrating a situation where pagedata is converted from a line sequence into a block sequence.

FIGS. 9A and 9B are explanatory diagrams for describing line sequencevector data according to the first exemplary embodiment of the presentdisclosure.

FIGS. 10A and 10B are explanatory diagrams for describing block sequencevector data according to the first exemplary embodiment of the presentdisclosure.

FIGS. 11A and 11B are explanatory diagrams for describing the linesequence vector data according to the first exemplary embodiment of thepresent disclosure.

FIGS. 12A and 12B are explanatory diagrams for describing the blocksequence vector data according to the first exemplary embodiment of thepresent disclosure.

FIGS. 13A and 13B are explanatory diagrams for describing line sequenceand block sequence raster data according to the first exemplaryembodiment of the present disclosure.

FIG. 14 is a flow chart illustrating edge information analysisprocessing according to the first exemplary embodiment of the presentdisclosure.

FIG. 15 is an explanatory diagram illustrating an edge informationanalysis table according to the first exemplary embodiment of thepresent disclosure.

FIGS. 16A to 16C are explanatory diagrams for describing processing ofcalculating the number of closed regions and the number of objects inthe closed regions according to the first exemplary embodiment of thepresent disclosure.

FIG. 17 is a flow chart illustrating block sequence conversion methodselecting processing according to the first exemplary embodiment of thepresent disclosure.

FIG. 18 is a flow chart illustrating memory size calculation processingin block sequence conversion processing based on a vector systemaccording to the first exemplary embodiment of the present disclosure.

FIG. 19 is a flow chart illustrating image forming processing of theimage processing apparatus according to a second exemplary embodiment ofthe present disclosure.

FIG. 20 is a flow chart illustrating the rendering processing accordingto the second exemplary embodiment of the present disclosure.

FIG. 21 is a flow chart illustrating the edge information analysisprocessing according to the second exemplary embodiment of the presentdisclosure.

FIG. 22 is a flow chart illustrating the block sequence conversionmethod selecting processing according to the second exemplary embodimentof the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Hereinafter, exemplary embodiments disclosed in the presentspecification will be described with reference to the drawings.

First Exemplary Embodiment

FIGS. 1A and 1B are block diagrams illustrating examples ofconfigurations of a system and an image processing apparatus accordingto the present exemplary embodiment. The system illustrated in FIG. 1Ais provided with a host computer 101, a mobile terminal 102, a printerserver 103, and an image processing apparatus 110. The host computer101, the mobile terminal 102, the printer server 103, and the imageprocessing apparatus 110 are connected to one another via a LAN 105.

The host computer 101, the mobile terminal 102, and the printer server103 generate page description language (PDL) data corresponding to pageimage data indicating page information to be printed on the basis of auser operation and transmit the PDL data to the image processingapparatus 110. The image processing apparatus 110 performs printingprocessing on the basis of the received PDL data. The image processingapparatus 110 may be any one of printers among a multi function printer(MFP) and a single function printer (SFP). The image processingapparatus 110 may also be a printer other than the MFP or the SFP.

With reference to FIG. 1B, a hardware configuration of the imageprocessing apparatus 110 according to the present exemplary embodimentwill be described. As illustrated in FIG. 1B, the image processingapparatus 110 includes a printer unit 111, an operation unit 113, and acontroller 200. The controller 200 includes a CPU 220, a ROM 221, a RAM222, a storage device 223, an operation unit interface (I/F) 225, anetwork I/F 226, an image bus I/F 228, a raster image processor (RIP)231, and a device I/F 232.

The CPU 220, the ROM 221, the RAM 222, the storage device 223, theoperation unit I/F 225, the network I/F 226, and the image bus I/F 228are connected to one another via a system bus 227. The image bus I/F228, the RIP 231, and the device I/F 232 are connected to one anothervia an image bus 230. In addition, the system bus 227 and the image bus230 are connected to each other via the image bus I/F 228.

The printer unit 111 is an image output device. While the imageprocessing apparatus 110 is connected to the LAN 105, the imageprocessing apparatus 110 performs control so as to input and output thePDL data or device information via the LAN 105. Image data generated bythe image processing apparatus 110 is input to the printer unit 111 viathe device I/F 232, and the input image data is output to a recordingmedium such as paper.

The CPU 220 is a central processing unit configured to control theentirety of the image processing apparatus 110.

The ROM 221 is a boot ROM and stores a boot program of the system.

The RAM 222 is a system work memory used for the CPU 220 to operate. TheRAM 222 also temporarily stores PDL data transmitted from an externalapparatus, intermediate data generated in the image processing apparatus110 at the time of the printing processing, or input data. In addition,the RAM 222 functions as a work area corresponding to an operation areaused to perform the rendering processing.

The storage device 223 is, for example, a hard disc drive. The storagedevice 223 stores system software for various processings and the PDLdata transmitted from the external apparatus.

The operation unit I/F 225 is an interface for connecting the operationunit 113 including a display screen on which various menus, printingdata information, and the like can be displayed to the system bus 227.The operation unit I/F 225 outputs operation screen data to theoperation unit 113. In addition, the operation unit I/F 225 transfersinformation input to the operation unit 113 by an operator to the CPU220.

The network I/F 226 is connected to the LAN 105 illustrated in FIGS. 1Aand 1B. The network I/F 226 performs input and output of informationwith the external apparatus via the LAN 105.

The image bus I/F 228 is an interface for connecting the system bus 227to the image bus 230 that transfers the image data at a high speed andis a bus bridge configured to convert a data structure.

The RIP 231 analyzes the PDL data or the intermediate data (displaylist) to be developed into an image.

The device I/F 232 is an interface for connecting the printer unit 111to the image bus 230. The device I/F 232 performssynchronous/asynchronous conversion of the image data. Softwareconfiguration of the image processing apparatus

FIG. 2 is a block diagram illustrating a software configuration of theimage processing apparatus 110 according to the present exemplaryembodiment. As illustrated in FIG. 2, the image processing apparatus 110includes a PDL data processing unit 201, an intermediate data generationunit 202, an image forming processing unit 203, a block sequenceconversion method selecting unit 204, a vector system block sequenceconversion unit 205, a raster system block sequence conversion unit 206,and a block sequence conversion memory 210.

The PDL data processing unit 201 obtains page information and objectinformation included in the page information from the PDL data receivedfrom the external apparatus such as the host computer 101. The PDL dataprocessing unit 201 hands over the obtained information to theintermediate data generation unit 202.

The intermediate data generation unit 202 generates intermediate data onthe basis of the page information and the object information receivedfrom the PDL data processing unit 201.

The block sequence conversion method selecting unit 204 selects a blocksequence conversion method of the data from a vector system and a rastersystem. The block sequence conversion method selecting unit 204determines one of the block sequence conversion method based on thevector system and the block sequence conversion method based on theraster system to perform the block sequence conversion by using asmaller memory on the basis of the intermediate data generated by theintermediate data generation unit 202. That is, the block sequenceconversion method selecting unit 204 determines one of timings beforeand after rendering to perform the block sequence conversion by using asmaller memory used for the conversion.

The block sequence conversion method selecting unit 204 selects theblock sequence conversion method on the basis of the determinationresult. The block sequence conversion method selecting unit 204 notifiesthe image forming processing unit 203 of the selected block sequenceconversion method. A detail of the detail of the block sequenceconversion method selecting processing will be described below.

The image forming processing unit 203 performs bitmap image data (rasterdata) generation processing (hereinafter, will be referred to as imageforming processing) from the intermediate data received from theintermediate data generation unit 202. In addition, to generate blocksequence raster data, the image forming processing unit 203 converts thedata into block sequence data on the basis of the block sequenceconversion method selected by the block sequence conversion methodselecting unit 204. When the block sequence conversion processing of thedata is performed, the image forming processing unit 203 causes thevector system block sequence conversion unit 205 or the raster systemblock sequence conversion unit 206 to execute processing of convertingthe data into the block sequence data.

In a case where the vector system is selected as the block sequenceconversion method, the image forming processing unit 203 stores thevector data internally used by the image forming processing unit 203 inthe block sequence conversion memory 210. The vector system blocksequence conversion unit 205 converts the vector data stored in theblock sequence conversion memory 210 into the block sequence vector dataand hands over the converted block sequence vector data to the imageforming processing unit 203. The image forming processing unit 203generates raster data by rendering the received block sequence vectordata. As a result, the block sequence raster data is generated. Theimage forming processing unit 203 applies image processing (such asrotation processing) on the generated block sequence raster data andgenerates the final bitmap image data to be output. A detail of theprocessing in the vector system block sequence conversion unit 205 willbe described below.

On the other hand, in a case where the raster system is selected as theblock sequence conversion method, the image forming processing unit 203stores the raster data obtained by rendering the intermediate data inthe block sequence conversion memory 210. The raster system blocksequence conversion unit 206 converts the raster data stored in theblock sequence conversion memory 210 into the block sequence raster datato be handed over to the image forming processing unit 203. The imageforming processing unit 203 applies the image processing (such as therotation processing) on the received block sequence raster data andgenerates the final bitmap image data to be output. A detail of theprocessing in the raster system block sequence conversion unit 206 willbe described below.

PDL Printing Processing in the Image Processing Apparatus

FIG. 3 is a flow chart illustrating the image forming processing of theimage processing apparatus 110 according to the present exemplaryembodiment. FIG. 3 illustrates a series of flows of the printingprocessing on the PDL data in the image processing apparatus 110. Theprocessing illustrated in FIG. 3 is executed by the CPU 220.Specifically, the CPU 220 loads a program for executing the processingillustrated in FIG. 3 from the ROM 221 to the RAM 222 to be executed,and the processing illustrated in FIG. 3 is executed. It should be notedthat a configuration may also be adopted in which a CPU, a ROM, and aRAM of an external apparatus (for example, the printer server 103)instead of the CPU 220, the ROM 221, and the RAM 222 of the imageprocessing apparatus 110 execute the processing illustrated in FIG. 3.

First, the PDL data is transmitted from the host computer 101, themobile terminal 102, or the printer server 103 to the image processingapparatus 110. Subsequently, the CPU 220 of the image processingapparatus 110 stores the received PDL data in the storage device 223.

Next, the CPU 220 obtains the PDL data stored in the storage device 223and performs analysis processing of the obtained PDL data (step S301).

Next, the CPU 220 performs generation processing of the intermediatedata used to generate the bitmap image data on the basis of theinformation of the analyzed PDL data (step S302). In the processing instep S302, two processings including edge information analysisprocessing and block sequence conversion method selecting processingcorresponding to features of the present exemplary embodiment areperformed in addition to intermediate data generation processing basedon a related-art technology.

Next, the CPU 220 performs the rendering processing accompanied by theblock sequence conversion processing on the basis of the generatedintermediate data and the selection result of the block sequenceconversion method selecting processing and generates bitmap image datarepresenting a page image (step S303). It should be noted that thegenerated bitmap image data is the block image data where the page imageis set in the block sequence, and subsequent image processing is appliedto this block image data in units of block.

Detail of Intermediate Data Generation Processing

FIG. 4 is a flow chart illustrating intermediate data generationprocessing according to the present exemplary embodiment. Theintermediate data generation processing according to the presentexemplary embodiment includes the edge information analysis processingand the block sequence conversion method selecting processingcorresponding to the features of the present exemplary embodiment asdescribed above.

First, the CPU 220 obtains a drawing object included in the PDL dataextracted by the PDL analysis processing in step S301 (step S401).

Next, the CPU 220 converts the drawing object obtained in step S401 intoa predetermined intermediate data format (step S402).

Next, the CPU 220 performs analysis processing of edge informationcorresponding to information indicating an outline (edge) of theobtained drawing object (step S403). At this time, the CPU 220 extractsinformation used to select the block sequence conversion method in therendering processing in step S303 from the edge information of thedrawing object. A detail of the processing in step S403 will bedescribed below.

Next, the CPU 220 determines whether or not the processing for thedrawing objects included in the PDL data, that is, all the drawingobjects in the page is ended (step S404). In a case where the processingis not ended, the CPU 220 returns to the processing in step S401. In acase where the processing is ended, the CPU 220 proceeds to theprocessing in step S405.

Next, the CPU 220 selects the block sequence conversion method on thebasis of the information extracted in step S403 from the edgeinformation (step S405). In the processing in step S405, the CPU 220determines one of the systems including the vector system and the rastersystem to perform the block sequence conversion by using a smallermemory consumption and determines one of the systems for the blocksequence conversion method. Then, the CPU 220 stores informationindicating the determined block sequence conversion method in a blocksequence conversion method table that will be described below. Thedetail of the block sequence conversion method selecting processing willbe described below.

Detail of the Rendering Processing

FIG. 5 is a flow chart illustrating rendering processing according tothe present exemplary embodiment (processing in step S303). Therendering processing according to the present exemplary embodimentincludes the block sequence conversion processing corresponding to thefeature of the present exemplary embodiment. FIGS. 6A to 6D areexplanatory diagrams for describing closed region data generated by therendering processing according to the present exemplary embodiment.

First, the CPU 220 performs generation processing of the closed regiondata corresponding to the vector data dealt in the rendering processingon the basis of the intermediate data generated in step S302 (stepS501).

With reference to FIGS. 6A to 6D, the closed region data correspondingto the vector data will be described. Herein, pages (pages 610 and 611illustrated in FIGS. 6B and 6C) including mutually overlapping threedrawing objects (objects 601 to 603 illustrated in FIG. 6A) will bedescribed as an example.

FIG. 6B schematically illustrates the page 610 including the objects 601to 603 having no transparent specifications and also a line 612 andvector data of the line 612 in the page 610. FIG. 6C schematicallyillustrates the page 611 including the objects 601 to 603 havingtransparent specifications and also a line 613 and vector data of theline 613 in the page 611.

The vector data of the line includes information indicating a start ofthe line (hereinafter, will be referred to as line start data) andinformation indicating a region sectioned by outlines of the objectswhich is called a closed region (hereinafter, will be referred to asclosed region data). As illustrated in FIG. 6B, the line 612 isconstituted by seven regions (closed regions 621 to 627) sectioned bythe outlines. Therefore, the vector data of the line 612 includes sevenpieces of closed region data corresponding to the closed regions 621 to627. Similarly, as illustrated in FIG. 6C, the line 613 is constitutedby seven regions (closed regions 631 to 637) sectioned by the outlines.Therefore, the vector data of the line 613 includes seven pieces ofclosed region data corresponding to the closed regions 631 to 637.

Hereinafter, the objects 601 to 603 may be denoted by objects #1 to #3in some cases. The lines 612 and 613 may be denoted by lines #1 and #2in some cases. The closed regions 621 to 627 may be denoted by closedregions #1 to #7 in some cases. The closed regions 631 to 637 may bedenoted by closed regions #11 to #17 in some cases.

FIG. 6D illustrates an example of a configuration of the closed regiondata included in the vector data. The closed region data illustrated inFIG. 6D is an example of the closed region data corresponding to theclosed regions 622 and 633 (the closed regions #2 and #13). Asillustrated in FIG. 6D, the closed region data includes lengthinformation indicating a width (length) of the closed region and objectnumber information indicating the number of objects included in theclosed region (hereinafter, will be simply referred to as the number ofobjects). The closed region data is accompanied by information of therespective objects included in the closed region (object data). Theobject data is information indicating an attribute, a color, or the likeof the object.

In step S501, the CPU 220 performs the generation processing of theclosed region data with regard to the band having a predeterminedheight, that is, the band having a predetermined number of lines.

Next, the CPU 220 determines whether or not the block sequenceconversion method corresponding to the currently processed band(hereinafter, will be referred to a as processing band) is the vectorsystem in accordance with the block sequence conversion method tablegenerated in the processing in step S405 (step S502). FIG. 7 is anexplanatory diagram illustrating an example of the block sequenceconversion method table according to the present exemplary embodiment.The block sequence conversion method table is a table indicating theblock sequence conversion method corresponding to each band in the page.Y illustrated in FIG. 7 denotes the number of pixels from an initialposition of the page in a sub scanning direction. For example, the bandregion #2 is a region (band) located at positions where pixels from theinitial position of the page in the sub scanning direction correspond tonumbers 32 to 63.

In a case where the block sequence conversion method is not the vectorsystem (step S502: NO), the CPU 220 shifts to the processing in stepS504.

In a case where the block sequence conversion method is the vectorsystem (step S502: YES), the CPU 220 temporarily stores the vector datacorresponding to the processing band generated by the processing in stepS501 in the block sequence conversion memory 210. Then, the CPU 220converts the vector data stored in the block sequence conversion memory210 into the block sequence (step S503). FIG. 8 is an explanatorydiagram illustrating a situation where the vector data is converted fromthe line sequence to the block sequence. A left drawing in FIG. 8illustrates the line sequence vector data. A right drawing in FIG. 8illustrates the block sequence vector data. When the vector data isgenerated from the intermediate data, the CPU 220 sequentially generatesthe closed region data in units of line from left (left in FIG. 8) toright (right in FIG. 8) with respect to a processing band 800. As aresult, the line sequence vector data is generated. In the processing instep S503, the CPU 220 converts the vector data from the line sequenceto the block sequence by dividing the line sequence vector data in unitsof block. According to the present exemplary embodiment, the processingband 800 is divided into two blocks including a block 806 and a block807.

Here, with reference to FIGS. 9A and 9B and FIGS. 10A and 10B,processing of converting the vector data of the page 610 illustrated inFIG. 6B from the line sequence to the block sequence will be described.FIGS. 9A and 9B are explanatory diagrams for describing the linesequence vector data according to the present exemplary embodiment.FIGS. 10A and 10B are explanatory diagrams for describing the blocksequence vector data according to the present exemplary embodiment.FIGS. 9A and 9B and FIGS. 10A and 10B illustrate a situation where thevector data corresponding to a processing band 801 of the page 610 isdivided into two blocks (a block 808 and a block 809).

In processing in step S503, first, the CPU 220 generates the closedregion data in the line sequence with respect to the processing band801. As a result, the line sequence vector data as illustrated in FIG.9B is generated. The CPU 220 stores the generated vector data in theblock sequence conversion memory 210. Subsequently, the CPU 220 readsout the line sequence vector data corresponding to the processing band801 stored in the block sequence conversion memory 210. At this time,the CPU 220 reads out the line sequence vector data in the blocksequence. As a result, the sequence of the closed region data isconverted into the block sequence as illustrated in FIG. 10B.

Here, with reference to FIGS. 11A and 11B and FIGS. 12A and 12B,processing of converting the vector data of the page 611 illustrated inFIG. 6C from the line sequence to the block sequence will be described.FIGS. 11A and 11B are explanatory diagrams for describing the linesequence vector data according to the present exemplary embodiment.FIGS. 12A and 12B are explanatory diagrams for describing the blocksequence vector data according to the present exemplary embodiment.FIGS. 11A and 11B and FIGS. 12A and 12B illustrate a situation where thevector data in a processing band 802 of the page 611 is divided into twoblock (a block 816 and a block 817).

First, the CPU 220 generates the closed region data in the line sequencewith respect to the processing band 802. As a result, the line sequencevector data is generated as illustrated in FIG. 12B. The CPU 220 storesthe generated vector data in the block sequence conversion memory 210.Subsequently, the CPU 220 reads out the line sequence vector datacorresponding to the processing band 802 stored in the block sequenceconversion memory 210. At this time, the CPU 220 reads out the linesequence vector data in the block sequence. As a result, the sequence ofthe closed region data is converted into the block sequence asillustrated in FIG. 12B.

Next, the CPU 220 executes rendering on the basis of the vector data andgenerates the raster data (step S504). The raster data is informationincluding CMYK data of the respective pixels in the page (hereinafter,will be also simply referred to as CMYK). It should be noted that, in acase where the block sequence conversion method is the vector system,since the vector data is converted into the block sequence in theprocessing in step S503, the raster data generated in the processing instep S504 is also generated in the block sequence. On the other hand, ina case where the block sequence conversion method is the raster system,since the processing in step S503 is not executed, the raster datagenerated in the processing in step S504 is generated in the linesequence. The raster data may also include pixel data other than CMYK.

Next, the CPU 220 performs processing of referring to the block sequenceconversion method table and determining whether or not the blocksequence conversion method corresponding to the processing band is theraster system (step S505).

In a case where the block sequence conversion method is not the rastersystem (step S505: NO), the CPU 220 shifts to the processing in stepS507.

In a case where the block sequence conversion method is the rastersystem (step S505: YES), the CPU 220 converts the raster data generatedin the line sequence in the processing in step S504 into the blocksequence (step S506). At this time, first, the CPU 220 temporarilystores the raster data corresponding to the processing band generated inthe processing in step S504 in the block sequence conversion memory 210.Then, the CPU 220 converts the raster data stored in the block sequenceconversion memory 210 from the line sequence into the block sequencesimilarly as in the block sequence conversion of the vector dataillustrated in FIG. 8.

Here, with reference to FIGS. 13A and 13B, processing of converting theraster data of the page 610 illustrated in FIG. 6B from the linesequence into the block sequence will be described. FIGS. 13A and 13Bare explanatory diagrams for describing the line sequence and blocksequence raster data according to the present exemplary embodiment.FIGS. 13A and 13B illustrate a situation where the raster datacorresponding to the processing band 801 of the page 610 is divided intothe two blocks (the block 808 and the block 809).

As described above, in a case where the block sequence conversion methodis the raster system, in the processing in step S504, the raster data isgenerated in the line sequence. A right drawing of FIG. 13A illustratesthe raster data corresponding to the processing band 801 generated inthe line sequence. In the processing in step S506, first, the CPU 220stores the raster data generated by the processing in step S504 in theblock sequence conversion memory 210. Subsequently, the CPU 220 readsout the raster data in the line sequence corresponding to the processingband 801 stored in the block sequence conversion memory 210. At thistime, the CPU 220 reads out the raster data in the line sequence in theblock sequence. As a result, the sequence of CMYK is converted into theblock sequence as illustrated in a right drawing of FIG. 13B. Herein,the page 610 including the objects having no transparent specificationhas been described as an example. However, since the pixel generationprocessing (processing in step S504) has been already executed in theprocessing in step S506, the size of the raster data is the sameirrespective of the presence or absence of the transparentspecification. Therefore, descriptions in a case where the transparentspecification is present will be omitted.

Next, the CPU 220 determines whether or not the processing is ended forall the bands included in the page image data (step S507). When it isdetermined that the processing is not ended (step S507: NO), the CPU 220returns to the processing in step S501. When it is determined that theprocessing is ended (step S507: YES), the CPU 220 ends the processing.

Detail of the Edge Information Analysis Processing

FIG. 14 is a flow chart illustrating the edge information analysisprocessing according to the present exemplary embodiment (processing instep S403). With regard to the edge information analysis processing, theCPU 220 extracts information used to select the block sequenceconversion method from the edge information of the drawing object in therendering processing (processing in step S303). The CPU 220 stores theextracted information in an edge information analysis table. FIG. 15 isan explanatory diagram illustrating the edge information analysis tableaccording to the present exemplary embodiment. As illustrated in FIG.15, the edge information analysis table is a table that stores anaccumulated total of heights of edges of the drawing objects existing inthe band region for each band region, an accumulated total of the areasof the regions having the transparent specification in this band region(hereinafter, will be referred to as a transparent specification area),and information for distinguishing the edges of the drawing objectsexisting in this band region (hereinafter, will be referred to as anassociated edge ID). The edge information analysis table is stored, forexample, in the RAM 222.

FIGS. 16A to 16C respectively illustrate examples of part of the page(region including a plurality of lines). It should be noted that, inFIGS. 16A to 16C, a height (width in a vertical direction in FIGS. 16Ato 16C) of each of lines (lines #1 to #5) is 1 pixel. The objects 601 to603 having the height of 3 pixels having no transparent specificationare overlapped with one another in the region illustrated in FIG. 16A.The number of closed regions in the region illustrated in FIG. 16A(hereinafter, will be also referred to as a closed region number) is 23pieces as illustrated in a middle drawing of FIG. 16A. On the otherhand, the height of each of the objects 601 to 603 is 3 pixels, and theheight of each of the edges indicating the outlines of the objects isalso 3 pixels. Therefore, the accumulated total of the heights of theedges is 23 pixels as illustrated in a right drawing of FIG. 16A. Inthis manner, the number of closed regions (23 pieces) has a feature ofbeing matched with the accumulated total of the heights of the edges (23pixels). Therefore, while the accumulated total of the heights of theedges is obtained, it is possible to estimate the number of closedregions. It should be noted that, since the edge of the page left end isan edge that creates a closed region, the height of the edge of the pageleft end is also added to the accumulated total of the edges accordingto the present exemplary embodiment. On the other hand, since the edgeof the page right end does not create a closed region within a pagerange, the height thereof is not included in the accumulated total ofthe heights of the edges. In the right drawing of FIG. 16A, the edge ofthe page left end and the edge indicating the start of the object areassigned with downward arrows. The edge indicating the end of the objectis assigned with an upward arrow. When a size of the vector datacorresponding to the processing band is estimated, the number of closedregions in the vector data is provided. For this reason, according tothe present exemplary embodiment, the heights of the edges areaccumulated to calculate the number of closed regions.

Here, a detail of the edge information analysis processing illustratedin FIG. 14 will be described.

First, the CPU 220 calculates a height of the edge of the drawing objectfrom the intermediate data generated in the processing in step S402(step S1401). Then, the CPU 220 updates the edge information analysistable with the calculated value. Specifically, the CPU 220 adds thecalculated value to the accumulated total of the heights of the edges inthe band region where the drawing objects exist.

Next, the CPU 220 determines whether or not the intermediate datagenerated in step S402 is a bitmap character (step S1402). In a casewhere the intermediate data is not the bitmap character (step S1402:NO), the CPU 220 shifts to the processing in step S1404.

In a case where the intermediate data is the bitmap character (stepS1402: YES), the CPU 220 calculates a height of the edge of the outlineof the bitmap character to be added to the accumulated total of theheights of the edges of the drawing objects (step S1403). In a casewhere the drawing object is the bitmap character, only the outlines ofboth ends of the bitmap edge (part assigned with outlined arrows in aleft drawing of FIG. 16B) are generated as the intermediate data in theprocessing in step S402. However, the outlines of the character are alsoextracted in the rendering processing as illustrated in a middle drawingof FIG. 16B, and the rendering processing is performed with respect to25 pieces of closed regions. In this manner, in a case where the drawingobject is the bitmap character, the number of closed regions is 25 incontrast to the accumulated total of the heights of the edges (15pixels), and therefore the number of closed regions is not matched withthe accumulated total of the heights of the edges. That is, in a casewhere the drawing object is the bitmap character, the number of closedregions is not precisely obtained on the basis of a simply accumulatedtotal of the heights of the edges. In view of the above, according tothe present exemplary embodiment, in a case where the drawing object isthe bitmap character, the CPU 220 analyzes the bitmap character toextract the edges of the character and calculates the precise number ofclosed regions. Specifically, the CPU 220 determines a boundary betweenthe bitmap character and another drawing object or a background in eachline as the edge of this bitmap character to be extracted. In theexample of FIG. 16B, the CPU 220 extracts a region where white and blackare reversed in a main scanning direction (part assigned with an outlinearrow illustrated in a right drawing of FIG. 16B) as the edge. The CPU220 adds the accumulated total of the heights of the edges of theextracted bitmap character (10 pixels) to the accumulated total of theheights of the edges of the drawing objects. As a result, the number ofclosed regions (25 pieces) is equal to the total value of the heights ofthe edges (15 pixels+10 pixels), and the precise number of closedregions is calculated. It should be noted that a related-art technologymay be used for the edge extraction processing.

Next, the CPU 220 determines whether or not the drawing objectcorresponding to the intermediate data generated in the processing instep S402 has the transparent specification (step S1404). In a casewhere the drawing object has no transparent specification (step S1404:NO), the CPU 220 shifts to the processing in step S1406.

In a case where the drawing object has the transparent specification(step S1404: YES), the CPU 220 calculates the area of a bounding box ofthe intermediate data generated in the processing in step S402 andupdates the edge information analysis table with the calculated value.Specifically, the CPU 220 adds the calculated value to an accumulatedtotal of the transparent specification areas of the band region wherethe drawing objects exist (step S1405). The accumulated total of thetransparent specification areas is used in calculation processing for amemory size in the block sequence conversion processing based on thevector system which will be described below. For this reason, accordingto the present exemplary embodiment, the accumulated total of thetransparent specification areas is calculated in the above-describedmanner.

Finally, the CPU 220 associates the band crossed by the edge of thedrawing object with the ID of this edge to be registered in the edgeinformation analysis table (step S1406). Specifically, the CPU 220registers the ID of this edge in the associated edge ID of the bandcrossed by this edge as a link list. The associated edge ID is used incalculation processing for the object number (processing in step S1803)which will be described below.

Detail of the Block Sequence Conversion Method Selecting Processing

FIG. 17 is a flow chart illustrating the block sequence conversionmethod selecting processing according to the present exemplaryembodiment (processing in step S405).

First, the CPU 220 calculates a band memory (memory size of the rasterdata for one band) which is used when the raster system is selected asthe block sequence conversion method (step S1701). Since the raster datais uniformly obtained from the following expression, the processing isperformed at only the initial position of the page.Band memory used in the block sequence conversion processing based onthe raster system=Page width×Height of the band (height of theblock)×Gray scale per channel×The number of channels per pixel  (1)

Next, the CPU 220 calculates a band memory (memory size of the vectordata in the processing band) which is used when the vector system isselected as the block sequence conversion method from the followingexpression (step S1702).Band memory used in the block sequence conversion processing based onthe vector system=Size total of the closed region data included in theband+Size total of the object data in the closed region included in theband  (2)

The size total of the closed region data included in the band and thesize total of the object data in the closed region included in the bandin Expression 2 are calculated from Expression 3 and Expression 4described below.

Next, the CPU 220 compares a magnitude relationship of the memory sizesderived from the processing in step S1701 and the processing in stepS1702 and determines whether or not the block sequence conversion isperformed by using a smaller memory consumption on the basis of thevector system than the raster system (step S1703).

In a case where the memory consumption is smaller on the basis of thevector system than the raster system (step S1703: YES), the CPU 220selects the vector system as the block sequence conversion method in theprocessing band (step S1704). Specifically, the CPU 220 specifies thevector system as the block sequence conversion method in this band ofthe block sequence conversion method table used in the renderingprocessing (processing in step S303).

In a case where the memory consumption is not smaller on the basis ofthe vector system than the raster system (step S1703: NO), the CPU 220selects the raster system as the block sequence conversion method in theprocessing band (step S1705). Specifically, the CPU 220 specifies theraster system as the block sequence conversion method in this band ofthe block sequence conversion method table used in the renderingprocessing (processing in step S303).

Next, the CPU 220 determines whether or not the processing is ended forall the bands in the page (step S1706). In a case where the processingis not ended (step S1706: NO), the CPU 220 returns to the processing instep S1702. In a case where the processing is ended (step S1706: YES),the CPU 220 ends the processing.

Detail of Memory Size Calculation Processing at the Time of the BlockSequence Conversion Processing Based on the Vector System

FIG. 18 is a flow chart illustrating memory size calculation processingin the block sequence conversion processing (processing in step S1702)based on the vector system according to the present exemplaryembodiment.

First, the CPU 220 calculates a size total of the closed region dataincluded in the processing band (step S1801). The size total of theclosed region data included in the processing band is calculated fromthe following expression.Size total of the closed region data included in the band=The number ofclosed regions included in the band×Size per data piece of the closedregion data  (3)

As described above, since the number of closed regions is matched withthe accumulated total of the heights of the edges, the accumulated totalof the heights of the edges is used as the number of closed regionsincluded in the band in Expression 3. At this time, the CPU 220 obtainsthe accumulated total of the heights of the edges corresponding to theprocessing band from the edge information analysis table.

The size per data piece of the closed region data is a uniquelydetermined value. For example, in a case where this size is 8 bytes andthe processing band corresponds to the band region #1 in the edgeinformation analysis table illustrated in FIG. 15, the size per datapiece of the closed region data in this processing band is calculated as32×8=256 bytes.

Next, the CPU 220 determines whether or not the drawing object havingthe transparent specification exists in the processing band (stepS1802). Herein, the CPU 220 obtains an accumulated total of thetransparent specification areas in this band from the edge informationanalysis table and determines whether or not the obtained value is lowerthan or equal to a previously set predetermined threshold (for example,Th0=0).

In a case where the accumulated total of the transparent specificationareas is higher than the threshold (step S1802: YES), the CPU 220determines that the drawing object having the transparent specificationexists in the processing band. Then, the CPU 220 shifts to theprocessing in step S1803 which will be described below.

In a case where the accumulated total of the transparent specificationareas is lower than or equal to the threshold (step S1802: NO), the CPU220 determines that the drawing object having the transparentspecification does not exist, and the number of overlapped drawingobjects in all the closed regions in the processing band is lower thantwo. That is, the CPU 220 determines that the drawing objects in theprocessing band are not overlapped with one another. Then, the CPU 220performs the generation processing of the actual closed region data withrespect to the processing band on the basis of the intermediate datagenerated in the processing in step S302 (step S1804). At this time, theCPU 220 extracts the edge information indicating the outline of thedrawing object included in the processing band from the intermediatedata. Then, the CPU 220 performs the generation processing of the closedregion data in the processing band on the basis of the positioninformation of the edge included in the edge information and thedetailed information of the outline, and furthermore the objectinformation associated with the outline and the information indicatinghow the objects are overlapped with one another. It should be notedhowever that the processing in step S1804 is executed only in a casewhere the drawing object having the transparent specification does notexist, and therefore the processing related to the overlapping where thetransparent specification is taken into account is not performed in theprocessing in step S1804. In this manner, the more simplified generationprocessing of the closed region data is performed in the processing instep S1804 as compared with the processing in step S501 described above.As a result, the number of drawing objects can be obtained by adding thenumbers of objects of the respective pieces of actually generated closedregion data to one another. It should be noted that a related-arttechnology is used with regard to the generation processing of theclosed region data.

Here, the processing in step S1803 will be described. In a case wherethe drawing object having the transparent specification exists in theprocessing band, the overlapping of the drawing objects is to be takeninto account. For this reason, when the closed region data is actuallygenerated similarly as in the processing in step S1804, processing loadmay be increased in some cases. In view of the above, the CPU 220calculates the number of drawing objects in the processing band in theprocessing in step S1803 in the following manner. First, as illustratedin a middle drawing in FIG. 16C, the CPU 220 holds informationindicating whether each edge in the processing band is an edgeindicating start or end of the drawing object as a table. The middledrawing in FIG. 16C indicates whether edges 1651 to 1657 illustrated ina left drawing in FIG. 16C are edges indicating the start or end of thedrawing object. It should be noted that the edges 1651 to 1657 arerespectively assigned with edge IDs 0 to 6. The CPU 220 obtainsinformation stored while being associated with the associated edge ID inthe processing band from the table illustrated in the middle drawing inFIG. 16C. Then, the CPU 220 determines whether or not each edge in theprocessing band is the edge indicating the start of the drawing objector the edge indicating the end of the drawing object from the obtainedinformation. Subsequently, as illustrated in a right drawing of FIG.16C, the CPU 220 scans the line in the processing band to calculate thenumber of drawing objects. Specifically, when the edge indicating thestart of the drawing object is detected, the CPU 220 adds 1 to thenumber of drawing objects. On the other hand, when the edge indicatingthe end of the drawing object is detected, the CPU 220 subtracts 1 fromthe number of drawing objects. The CPU 220 repeatedly performs thisprocessing for the number of the heights of the band, so that the numberof drawing objects in the processing band is calculated. It should benoted that the threshold (Th0 described above) used for determiningwhether or not the drawing object having the transparent specificationexists may be arbitrarily specified by a user.

The CPU 220 calculates the size total of the object data in the closedregions included in the processing band on the basis of the total of thenumbers of objects in the closed regions included in the processing bandobtained in the processing in step S1803 or S1804 by using the followingexpression.Size total of the object data in the closed regions included in theband=Total of the numbers of objects in the closed regions included inthe band×Size per data piece of the object data included in the closedregions  (4)

Finally, the CPU 220 assigns the values calculated by Expression 3 andExpression 4 to Expression 2 and calculates a memory size of the vectorsystem (step S1805).

As described above, according to the first exemplary embodiment, amemory size used in the block sequence conversion processing based onthe vector system and a memory size used in the block sequenceconversion processing based on the raster system are compared with eachother to select the block sequence conversion method using the stillsmaller memory consumption. As a result, irrespective of a content ofthe page image data corresponding to the target of the renderingprocessing, it is possible to perform the block sequence conversionprocessing at an optimal buffer memory size. For example, in a casewhere the page image data includes a large number of the drawing objectsand the size of the vector data may increase, since the block sequenceconversion method based on the raster system is selected, it is possibleto suppress the memory consumption at the time of the block conversionprocessing. In this manner, according to the present exemplaryembodiment, the optimal block sequence conversion method can be selectedin the block sequence conversion processing, and it is possible toperform the block sequence conversion processing by using the stillsmaller buffer memory size.

In addition, according to the present exemplary embodiment, when thenumber of drawing objects in the processing band is calculated, in acase where in the drawing object having the transparent specificationexists in the processing band (a case where it is determined as YES instep S1802), the number of objects is calculated on the basis of thedetection results of the edges indicating the start and end of thedrawing objects. Therefore, it is possible to further reduce theprocessing load when the memory size used at the time of the blocksequence conversion processing based on the vector system is derived. Itshould be noted that, in a case where the drawing object having thetransparent specification does not exist in the processing band (a casewhere it is determined as NO in step S1802), the flow may shift to theprocessing in step S1803 where the closed region data is not generatedwithout shifting to the processing in step S1804. According to theabove-described exemplary embodiment, it is possible to further reducethe processing load when the memory size used at the time of the blocksequence conversion processing based on the vector system is derived.

Second Exemplary Embodiment

According to the first exemplary embodiment, the image processingapparatus that performs the edge information analysis processing and theblock sequence conversion method selecting processing corresponding tothe features of the present exemplary embodiment in the intermediatedata generation processing (processing in step S302) is used as anexample. According to the present exemplary embodiment, the imageprocessing apparatus that performs the edge information analysisprocessing and the block sequence conversion method selecting processingcorresponding to the features of the present exemplary embodiment in therendering processing (processing in step S303) will be used as anexample. It should be noted that, since a hardware configuration and asoftware configuration of the image processing apparatus according tothe present exemplary embodiment are similar to those of the imageprocessing apparatus 110 according to the first exemplary embodiment,the descriptions thereof will be omitted.

Printing Processing of the Transferred PDL in the Image ProcessingApparatus

FIG. 19 is a flow chart illustrating the image forming processing of theimage processing apparatus according to the present exemplaryembodiment. FIG. 19 illustrates a series of flows of the printingprocessing on the PDL data of the image processing apparatus 110according to the present exemplary embodiment. The processingillustrated in FIG. 19 is executed by the CPU 220. Specifically, the CPU220 loads a program for executing the processing illustrated in FIG. 19from the ROM 221 into the RAM 222 to be executed, so that the processingillustrated in FIG. 19 is executed. It should be noted that aconfiguration may also be adopted in which a CPU, a ROM, and a RAM ofthe external apparatus (for example, the printer server 103) execute theprocessing illustrated in FIG. 19 instead of the CPU 220, the ROM 221,and the RAM 222 of the image processing apparatus 110.

First, the PDL data is transmitted from the host computer 101, themobile terminal 102, or the printer server 103 to the image processingapparatus 110. Subsequently, the CPU 220 of the image processingapparatus 110 stores the received PDL data in the storage device 223.

Next, the CPU 220 obtains the PDL data stored in the storage device 223and performs analysis processing of the obtained PDL data (step S1901).

Next, the CPU 220 performs generation processing for the intermediatedata used to generate the bitmap image data on the basis of the analyzedinformation of the PDL data (step S1902). In the processing in stepS1902, intermediate data generation processing based on a related-arttechnology is performed.

Next, the CPU 220 performs the edge information analysis processing andthe block sequence conversion method selecting processing correspondingto the features of the present exemplary embodiment. Furthermore, theCPU 220 performs the rendering processing accompanied by the blocksequence conversion processing in accordance with the generatedintermediate data and the selected block sequence conversion methodselecting processing and generates the bitmap image data representingthe page image (step S1903). It should be noted that the generatedbitmap image data is the block image data where the page image is set inthe block sequence, and subsequent image processing is applied to thisblock image data in units of block.

Detail of the Rendering Processing

FIG. 20 is a flow chart illustrating the rendering processing accordingto the present exemplary embodiment (processing in step S1903). FIG. 21is a flow chart illustrating the edge information analysis processingaccording to the second exemplary embodiment of the present disclosure.The rendering processing according to the present exemplary embodimentincludes the edge information analysis processing, the block sequenceconversion method selecting processing, and the block sequenceconversion processing corresponding to the features of the presentexemplary embodiment.

First, the CPU 220 extracts information from the edge informationincluded in the intermediate data generated in step S1902 which is usedfor selecting whether to perform the block sequence conversionprocessing on the basis of the vector data or perform the block sequenceconversion processing on the basis of the raster data (step S2001).Here, a detail of the processing in step S2001 will be described. FIG.21 is a flow chart illustrating the edge information analysis processingaccording to the second exemplary embodiment of the present disclosure(processing in step S2001). As illustrated in FIG. 21, the CPU 220generates the closed region data with regard to a previously setpredetermined number of lines (step S2101). It should be noted that thepredetermined number mentioned herein is a value lower than or equal tothe number of all lines in the band. That is, the predetermined numberof lines refers to all or part of lines in the processing band. At thistime, the CPU 220 extracts the edge information indicating the outlineof the drawing object included in the predetermined number of lines inthe processing band from the intermediate data. Then, the CPU 220performs the generation processing of the closed region data with regardto the predetermined number of lines in the processing band on the basisof position information of the edge included in the edge information anddetailed information of the outline. It should be noted that, in thegeneration processing of the closed region data in step S2101, objectinformation associated with the outline and information indicating howthe objects are overlapped with one another are further used in additionto the position information of the edge included in the edge informationand the detailed information of the outline. Therefore, in thegeneration processing of the closed region data in step S2101, similarlyas in the above-described processing in step S501, the generationprocessing of the closed region data in which the overlapping of theobjects is taken into account is performed. As a result, the numbers ofobject of the respective pieces of actually generated closed region dataare added to one another so that the number of drawing objects can beobtained irrespective of the presence or absence of the transparentspecification. It should be noted that a related-art technology is usedwith regard to the generation processing of the closed region data. As aresult, a configuration is adopted in which the size of the closedregion data of the predetermined number of lines calculated in theabove-described processing is used, and this is utilized in thesubsequent block sequence conversion method selecting processing(processing in step S2002).

Next, the CPU 220 performs the block sequence conversion methodselecting processing performed in the rendering processing (processingin step S1903) on the basis of the information used for the blocksequence conversion method selecting processing obtained in step S2001(step S2002). Specifically, the CPU 220 determines one of the vectorsystem and the raster system to perform the block sequence conversion byusing a smaller memory. The CPU 220 selects one of the vector system andthe raster system as the block sequence conversion method on the basisof the determination result. The detail of the block sequence conversionmethod selecting processing will be described below.

Next, the CPU 220 performs the generation processing of the closedregion data corresponding to the vector data dealt in the renderingprocessing (step S2003). Since the generated closed region data isequivalent to the content illustrated in FIGS. 9A and 9B to FIGS. 12Aand 12B, the descriptions thereof will be omitted.

Next, the CPU 220 refers to the block sequence conversion table anddetermines whether or not the block sequence conversion methodcorresponding to the processing band is the vector system (step S2004).

In a case where the block sequence conversion method is not the vectorsystem (step S2004: NO), the CPU 220 shifts to the processing in stepS2006.

In a case where the block sequence conversion method is the vectorsystem (step S2004: YES), the CPU 220 temporarily stores the vector datacorresponding to the processing band generated in the processing in stepS2003 in the block sequence conversion memory 210. Then, the CPU 220converts the vector data stored in the block sequence conversion memory210 into the block sequence (step S2005). Since the processing in stepS2005 is equivalent to the processing in step S503 described above, thedescriptions thereof will be omitted.

Next, the CPU 220 performs the generation processing of the raster dataon the basis of the vector data (step S2006). It should be noted that,in a case where the block sequence conversion method is the vectorsystem, the vector data is converted into the block sequence in theprocessing in step S2005. Thus, the raster data generated in theprocessing in step S2006 is also generated in the block sequence. On theother hand, in a case where the block sequence conversion method is theraster system, the processing in step S2005 is not executed. Thus, theraster data generated in the processing in step S2006 is generated inthe line sequence.

Next, the CPU 220 performs processing of referring to the block sequenceconversion method table and determining whether or not the blocksequence conversion method corresponding to the processing band is theraster system (step S2007).

In a case where the block sequence conversion method is not the rastersystem (step S2007: NO), the CPU 220 shifts to the processing in stepS2009.

In a case where the block sequence conversion method is the rastersystem (step S2007: YES), the CPU 220 generates the raster data that hasbeen generated in the line sequence in step S2006 in the block sequence(step S2008). At this time, first, the CPU 220 temporarily stores thepixel data corresponding to the processing band generated in theprocessing in step S2006 in the block sequence conversion memory 210.Then, the CPU 220 converts the raster data stored in the block sequenceconversion memory 210 from the line sequence into the block sequence.Since the processing in step S2008 is equivalent to the processing instep S506 described above, the descriptions thereof will be omitted.

Next, the CPU 220 determines whether or not the processing is ended forall the bands included in the page image data (step S2009). When it isdetermined that the processing is not ended (step S2009: NO), the CPU220 returns to the processing in step S2001. When it is determined thatthe processing is ended (step S2009: YES), the CPU 220 ends theprocessing.

Detail of the Block Sequence Conversion Method Selecting Processing

FIG. 22 is a flow chart illustrating the block sequence conversionmethod selecting processing according to the second exemplary embodimentof the present disclosure (processing in step S2002).

First, the CPU 220 calculates the memory size of the raster data for thepredetermined number of lines when the raster system is selected as theblock sequence conversion method (step S2201). Since the raster data isuniformly obtained from the following expression, the processing isperformed at only the initial position of the page.Memory for the predetermined number of line used in the block sequenceconversion processing based on the raster system=Page width×Height ofthe predetermined number of lines×Gray scale per channel×The number ofchannels per pixel  (5)

Next, the CPU 220 calculates the memory size of the vector data for thepredetermined number of lines which is used when the vector system isselected as the block sequence conversion method (step S2202). Inactuality, the CPU 220 obtains the data size of the closed region datain the predetermined band generated in the processing in step S2101.

Next, the CPU 220 compares a magnitude relationship of the memory sizesderived from the processing in step S2201 and the processing in stepS2202 and determines whether or not the block sequence conversion isperformed by using a smaller memory consumption on the basis of thevector system than the raster system (step S2203).

In a case where the memory consumption is smaller on the basis of thevector system than the raster system (step S2203: YES), the flowproceeds to step S2204. In a case where the raster system uses thesmaller memory consumption, the flow proceeds to step S2205.

In a case where the memory consumption is smaller on the basis of thevector system than the raster system (step S2203: YES), the CPU 220selects the vector system as the block sequence conversion method in theprocessing band (step S2204).

In a case where the memory consumption is not smaller on the basis ofthe vector system than the raster system (step S2203: NO), the CPU 220selects the raster system as the block sequence conversion method in theprocessing band (step S2205).

Next, the CPU 220 performs processing of determining whether or not theprocessing is ended for all the bands in the page (step S2206). In acase where the processing is not ended (step S2206: NO), the CPU 220returns to the processing in step S2202. In a case where the processingis ended (step S2206: YES), the CPU 220 ends the processing.

As described above, the image processing apparatus according to thepresent exemplary embodiment determines the block sequence conversionprocessing system optimal in units of band by generating the closedregion data for the predetermined number of lines. Specifically, thememory size of the vector data and the memory size of the vector datafor the predetermined number of lines in the processing band arecompared with each other to select the block sequence conversion methodin this processing band. According to the above-described exemplaryembodiment, since it is sufficient when the closed region data isgenerated for part of the lines instead of all of the lines in in theprocessing band in the processing in step S2101, it is possible tofurther reduce the processing load. It should be noted that, accordingto the first exemplary embodiment too, the memory size of the vectordata and the memory size of the vector data for the predetermined numberof lines in the processing band may be compared with each other, and theblock sequence conversion method in this processing band may beselected. To realize the above-described exemplary embodiment, forexample, it is sufficient when the processing targets in the processingin steps S1701, S1702, and S1804 are set as part of the lines instead ofall of the lines in the band.

Other Embodiments

Embodiment(s) of the present invention can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the aspect of the embodiments has been described with reference toexemplary embodiments, it is to be understood that the aspect of theembodiments is not limited to the disclosed exemplary embodiments. Thescope of the following claims is to be accorded the broadestinterpretation so as to encompass all such modifications and equivalentstructures and functions.

This application claims the benefit of Japanese Patent Application No.2015-226939, filed Nov. 19, 2015, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An apparatus that performs image processing oninput image data in units of block, the apparatus comprising: one ormore processors configured to function as: a derivation unit configuredto derive a memory size used in conversion processing of converting dataalignment sequence in the image data from a line sequence into a blocksequence which is executed before a generation of raster data based onthe input image data and a memory size used in the conversion processingwhich is executed after the generation of the raster data; adetermination unit configured to determine whether to execute theconversion processing before the generation of the raster data or afterthe generation of the raster data, based on a comparison between thederived memory sizes; and a conversion unit configured to execute theconversion processing before the generation of the raster data or afterthe generation of the raster data based on the determination.
 2. Theapparatus according to claim 1, wherein the determination unitdetermines whether the conversion processing is executed before thegeneration of the raster data or executed after the generation of theraster data so as to decrease the memory size used in the conversionprocessing based on the derivation results.
 3. The apparatus accordingto claim 1, wherein the one or more processors is further configured tofunction as: a generation unit configured to generate vector data,wherein the input image data is page description language (PDL) data,wherein the generation unit generates the vector data based onintermediate data obtained by analyzing the PDL data, and wherein thederivation unit derives a size of the vector data as the memory sizeused in the conversion processing executed before the generation of theraster data.
 4. The apparatus according to claim 3, wherein thegeneration unit extracts closed regions sectioned by edges of drawingobjects for each line based on the intermediate data, and generatesclosed region data indicating a content of the closed region with regardto the respective extracted closed regions, generates object informationindicating a content of the drawing object with regard to the respectivedrawing objects in the closed regions, and generates vector data thatstores the generated closed region data and the object information, andwherein the derivation unit derives a size of the vector data from anumber of closed regions and an accumulated total of numbers of drawingobjects in the respective closed regions.
 5. The apparatus according toclaim 4, wherein the derivation unit estimates the number of closedregions included in the vector data from an accumulated total of heightsof the edges of the drawing objects included in the vector data.
 6. Theapparatus according to claim 5, wherein, in a case where the drawingobject is a bitmap character, the derivation unit determines a boundarybetween the bitmap character and another drawing object or a backgroundas an edge of the drawing object.
 7. The apparatus according to claim 4,wherein the derivation unit scans respective lines in the vector data todetect edges of the drawing objects and estimates an accumulated totalof the numbers of drawing objects in the respective closed regionsincluded in the vector data from a number of detected edges.
 8. Theapparatus according to claim 3, wherein the generation unit generatesthe vector data for each band region obtained by dividing the image databy a plurality of lines.
 9. The image processing apparatus according toclaim 1, wherein the derivation unit derives a memory size used in theconversion processing for a predetermined number of lines of the imagedata which is executed before the generation of the raster data and amemory size used in the conversion processing which is executed afterthe generation of the raster data, wherein the determination unitdetermines whether to execute the conversion processing on the imagedata before the generation of the raster data or after the generation ofthe raster data, based on a comparison between the derived memory sizes,and wherein the conversion unit executes the conversion processing onthe image data before the generation of the raster data or after thegeneration of the raster data based on the determination.
 10. A methodfor an apparatus that performs image processing on input image data inunits of block, the method comprising: deriving a memory size used inconversion processing of converting data alignment sequence in the imagedata from a line sequence into a block sequence which is executed beforegeneration of the raster data based on the input image data and a memorysize used in the conversion processing which is executed after thegeneration of the raster data; determining whether to execute theconversion processing before the generation of the raster data or afterthe generation of the raster data, based on a comparison between thederived memory sizes; and executing the conversion processing before thegeneration of the raster data or after the generation of the raster databased on the determination.
 11. The method according to claim 10,wherein the determining determines whether the conversion processing isexecuted before the generation of the raster data or executed after thegeneration of the raster data so as to decrease the memory size used inthe conversion processing based on the derivation results.
 12. Themethod according to claim 10, further comprising: generating vectordata, wherein the input image data is page description language (PDL)data, wherein the generating generates the vector data based onintermediate data obtained by analyzing the PDL data, and wherein thederiving derives a size of the vector data as the memory size used inthe conversion processing executed before the generation of the rasterdata.
 13. The method according to claim 12, wherein the generatingfurther includes: extracting closed regions sectioned by edges ofdrawing objects for each line based on the intermediate data, andgenerating closed region data indicating a content of the closed regionwith regard to the respective extracted closed regions, generatingobject information indicating a content of the drawing object withregard to the respective drawing objects in the closed regions, andgenerating vector data that stores the generated closed region data andthe object information, and wherein the deriving derives a size of thevector data from a number of closed regions and an accumulated total ofnumbers of drawing objects in the respective closed regions.
 14. Themethod according to claim 13, wherein the deriving estimates the numberof closed regions included in the vector data from an accumulated totalof heights of the edges of the drawing objects included in the vectordata.
 15. The method according to claim 10, wherein the deriving derivesa memory size used in the conversion processing for a predeterminednumber of lines of the image data which is executed before thegeneration of the raster data and a memory size used in the conversionprocessing which is executed after the generation of the raster data,wherein the determining determines whether to execute the conversionprocessing on the image data before the generation of the raster data orafter the generation of the raster data, based on a comparison betweenthe derived memory sizes, and wherein the executing conversion executesthe conversion processing on the image data before the generation of theraster data or after the generation of the raster data based on thedetermination.
 16. A method for generating raster data in units ofblock, the method comprising: converting data of a band in input imagedata in a predetermined data format into data of blocks in thepredetermined data format, and rasterizing the data of blocks in thepredetermined data format into raster data block by block, wherein themethod further comprises: deriving a first data size of data of one ormore scan lines included in the band of the input image data which is inthe predetermined data format, the first data size being variable inaccordance with at least transparencies of one or more objects on theone or more scan lines; comparing the derived first data size to asecond data size corresponding to a memory size to be used in conversionprocessing which is executed after a generation of raster data based onthe band of the input image data, wherein, in the conversion processing,data alignment sequence in the image data is converted from a linesequence into a block sequence; if the derived first data size isgreater than the second data size, converting data of a band includingthe one or more scan lines in the predetermined data format into rasterdata of the band including the one or more scan lines, and dividing theraster data of the band including the one or more scan lines into rasterdata of the blocks.
 17. The method according to claim 16, furthercomprising: if the derived first data size is greater than the seconddata size, converting data in the predetermined data format of a nextscan line of the one or more scan lines in the band into raster data ofthe next scan line and dividing the raster data of the next scan lineinto the raster data of the blocks.
 18. The method according to claim16, wherein a height of a band is equal to a height of a block, and awidth of the band is equal to a width of a plurality of blocks.
 19. Amethod for generating raster data of units of block, the methodcomprising: converting data of a band in input image data which is in apredetermined data format different from a raster data format into dataof blocks in the predetermined format; and rasterizing the data ofblocks in the predetermined data format into raster data of the blocks,block by block, wherein the method further comprises: obtaining a firstdata size of data of one or more lines included in the band in thepredetermined data format; if the obtained first data size is greaterthan a second data size corresponding to a memory size to be used inconversion processing which is executed after a generation of rasterdata based on the band of the input image data, wherein, in theconversion processing, data alignment sequence in the image data isconverted from a line sequence into a block sequence: rasterizing dataof a band in the predetermined data format including the one or morelines into raster data of the band including the one or more lines, lineby line; and dividing the raster data of the band into raster data ofblocks.
 20. The method according to claim 19, wherein, if the obtainedfirst data size is greater than the second data size, the rasterizingrasterizes the data of the band including the one or more lines in thepredetermined data format into the raster data of the band including theone or more lines, line by line, without converting the data of the bandincluding the one or more lines into data of the blocks in thepredetermined data format.
 21. The method according to claim 19, whereinthe data size of data which is in the predetermined data format of theone or more lines is variable in accordance with (i) a number of objectson the one or more lines, (ii) a positional relationship of the objectson the one or more lines and (iii) transparencies of the objects of theone or more lines.
 22. The method according to claim 19, wherein aheight of a band is equal to a height of a block, and a width of theband is equal to a plurality of blocks.